Recently, deep learning models have celebrated success in several computer vision tasks [1]}, [2]}, [3]}, which require large amounts of labeled data. However, collecting sufficient labels to train the model parameters involves considerable human effort, which is unacceptable in practice [4]}. Moreover, the labels of training data and testing data are usually disjoint, i.e., the labels in the testing phase are unseen in the training phase. In contrast, few-shot learning aims to learn the model parameter by a handful of training data (support sets) and adapt to the testing data (query sets) [5]}, [6]}, [7]}, effectively. In the testing phase, the model classifies the query set into the support sets according to the distance of their embeddings.
